#!/usr/bin/env python

# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse

CLASSES = ('__background__',
           'bathtub', 'bed', 'chair', 'desk',
           'dresser', 'monitor', 'night_stand', 'sofa', 'table',
           'toilet')

NETS = {'vgg16': ('VGG16',
                  'vgg16_onehot_iter_150000.caffemodel')}


def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal')
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]

        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
            )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                  thresh),
                  fontsize=14)
    plt.axis('off')
    plt.tight_layout()
    plt.draw()

def demo(net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the demo image
    ##im_file = os.path.join(cfg.DATA_DIR, 'modelnet_demo','room1k', image_name)
    im_file = os.path.join(cfg.DATA_DIR, 'modelnet_demo', image_name)
    ##im_file = os.path.join(cfg.DATA_DIR, 'modelnet_devkit', 'data','Images',image_name)
    im = cv2.imread(im_file)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, im)
    timer.toc()
    print ('Detection took {:.3f}s for '
           '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for a_cls_ind, a_cls in enumerate(CLASSES[1:]):
        ##Modf
        ##cls_ind += 1 # because we skipped background
       # cls_ind_b = a_cls_ind * 5 + 1
       # cls_ind_e = (a_cls_ind+1) * 5 + 1
       # cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
       # cls_scores = scores[:, cls_ind_b:cls_ind_e]
       # dets = np.hstack((cls_boxes,
       #                      cls_scores)).astype(np.float32)
       # keep = nms(dets, NMS_THRESH)
       for i in range(0,4): 
           cls_ind = a_cls_ind * 5 + i + 1 
           cls = a_cls + str(i)
           cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
           cls_scores = scores[:, cls_ind]
           dets = np.hstack((cls_boxes,
                             cls_scores[:, np.newaxis])).astype(np.float32)
           keep = nms(dets, NMS_THRESH)
           dets = dets[keep, :]
           vis_detections(im, cls, dets, thresh=CONF_THRESH)

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--cpu', dest='cpu_mode',
                        help='Use CPU mode (overrides --gpu)',
                        action='store_true')
    parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
                        choices=NETS.keys(), default='vgg16')

    args = parser.parse_args()

    return args

if __name__ == '__main__':
    cfg.TEST.HAS_RPN = True  # Use RPN for proposals

    args = parse_args()

    prototxt = os.path.join(cfg.MODELS_DIR, 'modeloneh', NETS[args.demo_net][0],
                            'faster_rcnn_end2end', 'test.prototxt')
    caffemodel = os.path.join(cfg.ROOT_DIR, 'output','faster_rcnn_end2end','train',
                              NETS[args.demo_net][1])

    if not os.path.isfile(caffemodel):
        raise IOError(('{:s} not found.\nDid you run ./data/script/'
                       'fetch_faster_rcnn_models.sh?').format(caffemodel))

    if args.cpu_mode:
        caffe.set_mode_cpu()
    else:
        caffe.set_mode_gpu()
        caffe.set_device(args.gpu_id)
        cfg.GPU_ID = args.gpu_id
    net = caffe.Net(prototxt, caffemodel, caffe.TEST)

    print '\n\nLoaded network {:s}'.format(caffemodel)

    # Warmup on a dummy image
    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
    for i in xrange(2):
        _, _= im_detect(net, im)

    im_names_real = ['real01.jpg','real02.jpg','real03.jpg','real04.jpg','real05.jpg','real06.jpg','real07.jpg','real08.jpg','real09.jpg','real10.jpg','real11.jpg','real12.jpg','real13.jpg','real14.jpg','real15.jpg','real16.jpg','real17.jpg','real18.jpg','real19.jpg','real20.jpg','real21.jpg','real22.jpg','real23.jpg','real24.jpg','real25.jpg','real26.jpg']
    im_names_gen = ['p277_1_0.jpg','p274_1_0.jpg','t69_1_0.jpg','t71_1_0.jpg','t92_1_0.jpg','t82_1_0.jpg','t83_1_0.jpg','t116_1_0.jpg','t114_1_0.jpg','t113_1_0.jpg','t94_1_0.jpg','t89_1_0.jpg','t76_1_0.jpg','t74_1_0.jpg','t48_1_0.jpg','t33_1_0.jpg']
    im_names_room = ['v1_1_0.jpg','v2_1_0.jpg','v3_1_0.jpg','v4_1_0.jpg','v5_1_0.jpg','v6_1_0.jpg','v7_1_0.jpg','v8_1_0.jpg','v9_1_0.jpg','v10_1_0.jpg','v11_1_0.jpg','v12_1_0.jpg','v14_1_0.jpg','v117_1_0.jpg']
    im_names = im_names_real
    for im_name in im_names:
        print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
        print 'Demo for data/demo/{}'.format(im_name)
        demo(net, im_name)

    ## plt.show()
    from matplotlib.backends.backend_pdf import PdfPages
    pp = PdfPages('/home/javier/rcnn/py-faster-rcnn/output/demo/multi.pdf')
    for i in plt.get_fignums():
	plt.figure(i)
	pp.savefig()
        #plt.savefig('/home/javier/rcnn/py-faster-rcnn/output/demo/figure%d.png' % i)
    pp.close()

